Search Results (506)

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Centered on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I²C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals. Full article

Water injection development represents the predominant development method for enhancing oil recovery (EOR) efficiency and achieving the balanced utilization of oil reservoirs. In light of the current situation of oilfield water injection technology, a comprehensive overview of the evolution of full-scale water injection technology is given, with particular emphasis on the influence of geological factors, technological advancements, and existing challenges. The principal issues currently encountered include an unequal distribution of layers, the complexity of subdivision, casing deformation, and damage to deep well equipment, which collectively impede the effective implementation of subdivision water injection development technology. The novelty of the research lies in the current development status of full-scale injection wells, which is not only reflected in the depth-scale, but also in the operational difficulty-scale. A thorough exploration of subdivision water injection development technologies has been conducted, and the applicability and limitations of these technologies in diverse reservoir conditions have been evaluated. The proposal is for intelligent injection technology to be adopted for medium–shallow heterogeneous wells, and for ball-pitching plugging profile control technology to be adopted for deep/horizontal/special condition wells. A comparative analysis was conducted to evaluate the characteristics, application scenarios, advantages, and disadvantages of intelligent injection technologies, demonstrating its intelligence, automation, and precision in the practical application. In regard to the ball-pitching plugging profile control technology, the design and performance of the plugging ball, the plugging mechanism, and the application effect were elucidated. Based on the existing challenges in the realm of water injection development, the research prospects for full-scale subdivision water injection development technologies were proposed, and the importance of interdisciplinary cooperation and the integration of artificial intelligence technology were also emphasized. This research would provide a technical foundation for increasing oil displacement efficiency, markedly augmenting EOR, and would also be imperative for improving the economic benefits and alleviating the global oil resource tension. Full article

With the advancement of oil and gas exploration and development technologies into deeper and ultra-deep reservoirs, complex geological conditions here render them highly susceptible to severe lost circulation. However, conventional bridging plugging methods struggle with large-sized lost circulation channels, while chemical gel plugging faces challenges such as low success rates and insufficient pressure-bearing capacity. To address this, a novel leak plugging method combining bridging and gel plugging is proposed herein. From structural stability and mechanical properties perspectives, the enhancing effect of nanomaterials on the gel system is revealed, and the synergistic mechanism of gel-bridging coupled plugging is elucidated. For the experimental setup, orthogonal experiments determined a base formulation with controllable gelation time: 10 wt% main agent, 2 wt% crosslinking agent, and a 1:3 pH regulator ratio. Introducing 1.0 wt% nanosilica enhanced gel properties, achieving 30 N strength at 120 °C aging. An optimized walnut shell bridging agent constructed the supporting skeleton, yielding a coupled plugging formulation with up to 8 MPa pressure for a 7 mm fracture. Lost circulation volume is controlled at 163 mL, outperforming single plugging methods. Research results demonstrate gel-bridging coupled plugging’s advantages for large fractures, providing new technical insights for severe lost circulation field construction. Full article

Polyurethane grouting materials are polymer materials formed through the reaction of polyisocyanates and polyols. They play important roles in underground engineering, tunnel construction, and mining due to their fast reaction rate, high bonding strength, and excellent impermeability. However, traditional polyurethane grouting materials have shortcomings such as high reaction heat release, high brittleness, and poor flame retardancy, which limit their applications in high-demand engineering projects. This paper systematically reviews the research progress on modified polyurethane grouting materials. Four major modification technologies are summarized: temperature reduction modification, flame retardant modification, mechanical enhancement, and environmental adaptability improvement. A multi-dimensional performance characterization system is established, covering slurry properties, solidified body performance, microstructure characteristics, thermal properties and flame retardancy, diffusion grouting performance, and environmental adaptability. The application effects of modified polyurethane grouting materials in grouting reinforcement, grouting water plugging, and grouting lifting are analyzed. Future development directions are projected. This review is particularly valuable for researchers and engineers working in tunneling, mining, geotechnical engineering, and infrastructure rehabilitation. Full article

Traditional petroleum engineering materials have problems such as single functionality and poor environmental adaptability in terms of lost circulation control and enhanced oil recovery. Supramolecular gels, with their dynamic reversible non-covalent network structure, demonstrate unique advantages in this regard. This paper classifies supramolecular gels into hydrogen bond type, metal coordination type, host–guest type, and electrostatic interaction type based on differences in crosslinking structures. It explains the construction principles and characteristics of each type of gel and analyses their application progress in petroleum engineering fields, such as lost circulation control in drilling, temporary plugging in fracturing, and profile control in enhanced oil recovery. It also discusses the advantages and disadvantages of different systems and future development directions. Research has shown that the molecular design strategy of supramolecular gels can effectively address technical challenges under complex conditions, offering new insights for oil and gas field development. Further optimization of their long-term stability and large-scale production technology is needed to advance their practical application. Full article

The steam flooding–gravity drainage technology has become one of the effective alternative development methods in the middle and later stages of thin-layer ultra-viscous oil steam throughput, with predicted recovery rate of over 50%. Currently, there is a lack of relevant technical research on the composite swallowing and spitting preheating stage. This is in response to the slow preheating of the oilfield and the large differences in connectivity between injection and production wells. The dynamic analysis method was used to analyze the key factors that restrict the efficient connectivity of steam throughput preheating. Based on this, a series steam throughput preheating efficient connectivity technologies were proposed. Physical simulation, numerical simulation, and other methods were used to characterize and demonstrate the technical principles and operating of the efficient connectivity technology. The research results were successfully applied to the super-viscous oil reservoirs of the Fengcheng oilfield in Xinjiang. The results show that the main factors severely limiting the balanced and rapid connectivity between injection and production wells are the limited radius of steam coverage, low utilization degree oil layers, and frequent unilateral steam breakthroughs. The reservoir expansion transformation has improved the reservoir properties along the horizontal section, increasing the utilization rate of the horizontal section from 51% to 90%, achieving rapid connectivity injection and production wells, and shortening the conventional throughput preheating cycle by 3–4 cycles. The group combination steam injection method achieved a centralized increase in thermal energy, with the inter-well connectivity changing from unidirectional to a broader area The reasonable steam injection intensity was 15 t/m, the regional temperature field increased from 83 °C to 112 °C, and the steam area expanded by approximately 10 m. The multi-medium composite technology achieved a dual increase in steam coverage and profile utilization, with the steam coverage radius increasing by 15 m and the oil reservoir profile utilization increasing by more than 30%. The temporary plugging and fracturing of the reservoir achieved the sealing of inherited breakthrough channels, directing the steam to unused areas, increasing the utilization rate to 89.2%, and shortening the throughput preheating cycle by 3 cycles. This series of technologies has achieved remarkable results in actual application in super-heavy oilfield, which has certain reference significance for the efficient and low-carbon development of heavy oil steam throughput reservoir turning into drive and release. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission. Full article

Esterification is a key transformation in the production of lubricants, pharmaceuticals, and fine chemicals. Conventional processes employing homogeneous acid catalysts suffer from limitations such as corrosive byproducts, energy-intensive separation, and poor catalyst reusability. This review provides a comprehensive overview of heterogeneous catalytic systems, including ion exchange resins, zeolites, metal oxides, mesoporous materials, and others, for improved ester synthesis. Recent advances in membrane-integrated reactors, such as pervaporation and nanofiltration, which enable continuous water removal, shifting equilibrium and increasing conversion under milder conditions, are reviewed. Dual-functional membranes that combine catalytic activity with selective separation further enhance process efficiency and reduce energy consumption. Enzymatic systems using immobilized lipases present additional opportunities for mild and selective reactions. Future directions emphasize the integration of pervaporation membranes, hybrid catalyst systems combining biocatalysts and metals, and real-time optimization through artificial intelligence. Modular plug-and-play reactor designs are identified as a promising approach to flexible, scalable, and sustainable esterification. Overall, the interaction of catalyst development, membrane technology, and digital process control offers a transformative platform for next-generation ester synthesis aligned with green chemistry and industrial scalability. Full article

In this work, we present a high-performance tapered quantum cascade laser (QCL) designed to achieve both high output power and low divergence angle. By integrating a tapered waveguide with a Fabry–Perot structure, significant improvements of tapered QCL devices in both output power and beam quality are demonstrated. The optimized 50 µm wide tapered QCL achieved a maximum output power of 2.76 W in pulsed operation with a slope efficiency of 3.52 W/A and a wall-plug efficiency (WPE) of 16.2%, while reducing the divergence angle to 13.01°. The device maintained a maximum power of 1.34 W with a WPE exceeding 8.2%, measured under room temperature and continuous wave (CW) operation. Compared to non-tapered Fabry–Perot QCLs, the tapered devices exhibited a nearly 10-fold increase in output power and over 200% improvement in WPE. This work provides a promising pathway for advancing mid-infrared laser technology, particularly for applications requiring high power, low divergence, and temperature stability. Full article

China has abundant shale oil and gas resources, which have become a critical pillar for future energy substitution. However, due to the highly heterogeneous nature and complex pore structures of shale reservoirs, traditional plugging agents face significant limitations in enhancing plugging efficiency and adapting to extreme wellbore environments. In response to the technical demands of nanoparticle-based plugging in shale reservoirs, this study systematically investigated the microscopic interaction mechanisms of nano-plugging agent shell polymers (Ployk) with various reservoir minerals under different temperature and salinity conditions using molecular simulation methods. Key parameters, including interfacial interaction energy, mean square displacement, and system density distribution, were calculated to thoroughly analyze the effects of temperature and salinity variations on adsorption stability and structural evolution. The results indicate that nano-plugging agent shell polymers exhibit pronounced mineral selectivity in their adsorption behavior, with particularly strong adsorption performance on SiO₂ surfaces. Both elevated temperature and increased salinity were found to reduce the interaction strength between the shell polymers and mineral surfaces and significantly alter the spatial distribution and structural ordering of water molecules near the interface. These findings not only elucidate the fundamental interfacial mechanisms of nano-plugging agents in shale reservoirs but also provide theoretical guidance for the precise design of advanced nano-plugging agent materials, laying a scientific foundation for improving the engineering application performance of shale oil and gas wellbore-plugging technologies. Full article

Wellbore strengthening is a widely applied technique to mitigate wellbore leakage during drilling operations in complex formations characterized by narrow mud weight windows. This method enhances the wellbore’s pressure-bearing capacity by using lost circulation materials (LCMs) to bridge natural or induced fractures. In recent years, advanced sealing technologies such as wellbore reinforcement have gradually been applied and developed, but their related influencing factors and mechanisms have not been deeply revealed. This article uses the Cohesive module of ABAQUS to establish a wellbore fracture sealing model. By establishing a porous elastic finite element model, the elastic mechanics theory of porous media is combined with finite element theory. Under the influence of factors such as anisotropy of geostress, reservoir elastic modulus, Poisson’s ratio, and fracturing fluid viscosity, the circumferential stress distribution of the wellbore after fracture sealing is simulated. The simulation results show that stress anisotropy has a significant impact on Mises stress. The greater the stress anisotropy, the more likely the wellbore sealing is to cause wellbore rupture or instability. Therefore, it is necessary to choose a suitable wellbore direction to avoid high stress concentration areas. The elastic modulus of the reservoir is an important parameter that affects wellbore stability and fracturing response, especially in high modulus reservoirs where the effect is more pronounced. Poisson’s ratio has a relatively minor impact. In fracturing and plugging design, the viscosity of fracturing fluid should be reasonably selected to balance the relationship between plugging efficiency and wellbore mechanical stability. In the actual drilling process, priority should be given to choosing the wellbore direction that avoids high stress concentration areas to reduce the risk of wellbore rupture or instability induced by plugging, specify targeted wellbore reinforcement strategies for high elastic modulus reservoirs; using models to predict fracture response characteristics can guide the use of sealing materials, achieve efficient bridging and stable sealing, and enhance the maximum pressure bearing capacity of the wellbore. By simulating the changes in circumferential stress distribution of the wellbore after fracture sealing, the mechanism of wellbore reinforcement was explored to provide guidance for mechanism analysis and on-site application. Full article

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

Tracking unmanned aerial vehicles (UAVs) in infrared video is an essential technology for the anti-UAV task. Given frequent UAV target disappearances caused by occlusion or moving out of view, global trackers, which have the unique ability to recapture targets, are widely used in infrared UAV tracking. However, global trackers perform poorly when dealing with large target scale variation because they cannot maintain approximate consistency between target sizes in the template and the search region. To enhance the scale adaptation of global trackers, we propose a plug-and-play scale adaptation enhancement module (SAEM). This can generate a scale adaptation enhancement kernel according to the target size in the previous frame, and then perform implicit scale adaptation enhancement on the extracted target template features. To optimize training, we introduce an auxiliary branch to supervise the learning of SAEM and add Gaussian noise to the input size to improve its robustness. In addition, we propose a one-stage anchor-free global tracker (OSGT), which has a more concise structure than other global trackers to meet the real-time requirement. Extensive experiments on three Anti-UAV Challenge datasets and the Anti-UAV410 dataset demonstrate the superior performance of our method and verify that our proposed SAEM can effectively enhance the scale adaptation of existing global trackers. Full article